Multipoint spectral data, such as mapping data or temporal change data obtained through sample-surface analysis, is subjected to processing to remove noise components therein.
Smoothing is one known technique to reduce the noise bandwidth of multipoint spectral data. After smoothing, however, the multipoint spectral data will have lower peaks in the spectra or will have wavenumber shifts occurring in the spectra.
Principal component analysis (PCA) is another known technique to remove noise from multipoint spectral data. With a conventional noise-component removing method based on PCA, multipoint spectral data that has been generated through measurements performed at multiple measurement points of a sample surface is separated into a plurality of principle components by PCA, and is then reconstructed to eliminate low-order components from the separated components. As a result, noise components are removed from the multipoint spectral data.
Such a noise-component removing method based on PCA has conventionally been applied, for example, to accumulated data (refer, for example, to Unexamined Japanese Patent Application Publication No. 2000-74826). The conventional noise-component removing method described in Unexamined Japanese Patent Application Publication No. 2000-74826 has also been applied to data corresponding to an area.
However, the inventors of the present invention have found that the noise-component removing method based on PCA may fail to reconstruct a spectrum that differs in shape from a large number of other multipoint spectra, or more specifically, may fail to reconstruct, for example, a spectrum representing a different substance contained at only one point in mapping data.
There have been no appropriate techniques to prevent low-order spectra (signal components) of multipoint spectral data from being lost during reconstruction of the multipoint spectral data, although conventional countermeasures to such signal loss have possibilities for improvement.